Due to rapid advances in sensor technology and computer science, automated systems are being put to new and increasingly sophisticated uses. Although automated systems are generally superior to human beings in traits such as speed and precision, human beings typically outperform automated systems in the application of contextual information and “human” experience to the interpretation of sensor data. For example, an automated system may falsely conclude that a human being no longer occupies a seat in a vehicle because the person sitting in the seat is in the process of pulling a sweater off over their head, leaving the head temporarily hidden from view. Automated systems in such a context are significantly less adept then even a young child. Various technologies are being developed to improve the ways in which automated systems interpret sensor data in various contexts. It would be desirable to enhance the success of those technologies by improving the technology for calibrating the sensor used to capture the sensor information for the automated system.
There are many different types of applications for which it could be advantageous for an automated system to accurately identify relevant information regarding sensor data. One example of an automated system using sensor information in a fully automated fashion is an “intelligent” safety restraint application in a vehicle that uses digital video footage to determine whether or not the deployment of an airbag should be precluded based upon the type of occupant and/or the proximity of the occupant to the deploying airbag. Many vehicle manufacturers are beginning to incorporate various forms of vehicle occupancy sensing (VOS) systems within the interior of the vehicle. Many of such systems utilize non-image based sensor information such as weight or acceleration (as measured by an accelerometer). Some VOS applications are using visual images of the seat area to make occupancy determinations. A small digital sensor or other form of sensor can be mounted within the interior of the vehicle. Such a sensor is typically capable of generating an image of the passengers sitting in the front seats of the vehicle, along with their immediate surroundings. By analyzing one or more successive images generated by the sensor, the VOS system can determine the presence and location of vehicle occupants, their general size, and even their movement upon sudden deceleration of the vehicle. This data can then be utilized to control “smart” or “intelligent” airbag systems. For example, the VOS system may determine that the vehicle occupant in the front passenger seat is smaller than a predefined size, and is thus presumably a child or a small adult. Subsequently, the airbag system may deactivate the passenger side airbag as deployment of the airbag could result in more harm than good to a relatively small occupant.
The above-described vehicle occupancy sensing (VOS) systems traditionally function by comparing images taken by the sensor to certain reference data maintained in the system. In order for this process to be successful, the system must know the spatial position and rotational orientation of the sensor in relation to the interior of the vehicle. Without this information, the system may be unable to accurately process the images acquired by the sensor. Ideally, both the spatial position and rotational orientation of the sensor are known upon its installation into the vehicle. However, due to translational and rotational tolerances in the installation process, the spatial position of the sensor may vary, for example, by ±10 mm in one or more of the three spatial axes. Similarly, installation tolerances may cause the rotational or angular orientation of the sensor to vary, for example, by ±5° degrees in one or more of the three angular axes. Beyond variations induced during the installation process, the spatial position and rotational orientation of the sensor is subject to change simply due to the everyday use of the vehicle. This is especially due to the rather harsh environment that vehicles are subject to, ranging from vibrations induced by the motor to sudden jolts to the structure of the vehicle due to varying road conditions.
These variances in the spatial positioning and rotational orientation of the sensor can be taken into account in order for vehicle occupancy sensing (VOS) systems and other sensor-based automated systems to function properly. As a VOS system must acquire a relatively large image within a very short distance, it typically utilizes a sensor having a wide-angle lens. However, as a result of using such a wide-angle lens, even small variations in the spatial position or angular rotation of the sensor can lead to significant variations in the sensor's field of view. For example, consider a VOS system whose sensor generates a digital image comprised of 640 by 480 pixels. A 5° degree variation in the angular orientation of the sensor could result in up to a 125 pixel shift in the image. As a result of this pixel shift in the sensor field of view, the effectiveness of the position and classification processes utilized by the VOS system can be severely degraded. Accordingly, a method and system to automatically calibrate the field of view of a sensor is needed so that the processes used by the VOS systems can be accurately adjusted to take into account arbitrary translational and angular displacements of the sensor.
The calibration system is not limited to vehicle safety restraint applications or even occupant sensing systems. Other examples of automated applications that can incorporate the functionality of the calibration system includes security devices, navigation technologies, medical instruments, and virtually any other application that involves functionality being provided in response to an image originating from sensor information. The calibration system can even be used in relation to sensors that are not imaged-based. For example, an ultrasound sensor generates an image, but the ultrasound sensor is not a light-based sensor.